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Identifying multiple influential spreaders based on generalized closeness centrality

Author

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  • Liu, Huan-Li
  • Ma, Chuang
  • Xiang, Bing-Bing
  • Tang, Ming
  • Zhang, Hai-Feng

Abstract

To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics—epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

Suggested Citation

  • Liu, Huan-Li & Ma, Chuang & Xiang, Bing-Bing & Tang, Ming & Zhang, Hai-Feng, 2018. "Identifying multiple influential spreaders based on generalized closeness centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2237-2248.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:2237-2248
    DOI: 10.1016/j.physa.2017.11.138
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    Citations

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    Cited by:

    1. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Zhang, Xiaohong & Li, Zhiying & Qian, Kai & Ren, Jianji & Luo, Junwei, 2020. "Influential node identification in a constrained greedy way," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Yi, Dongyun, 2018. "Effectively identifying multiple influential spreaders in term of the backward–forward propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 404-413.
    4. Wang, Ying & Zheng, Yunan & Shi, Xuelei & Liu, Yiguang, 2022. "An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    5. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    6. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    7. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    8. Shang, Jiaxing & Wu, Hongchun & Zhou, Shangbo & Zhong, Jiang & Feng, Yong & Qiang, Baohua, 2018. "IMPC: Influence maximization based on multi-neighbor potential in community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1085-1103.
    9. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.

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